{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2e2b9875",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.4142135623730950488016887242096980785696718753769480731766797379907324784621070388503875343276415727\n"
     ]
    }
   ],
   "source": [
    "from mpmath import mp;\n",
    "mp.dps = 100 + 1;\n",
    "# 打印圆周率\n",
    "print(mp.sqrt(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8be04380",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integer\n",
      "Even\n"
     ]
    }
   ],
   "source": [
    "num = float(input(\"Enter a number: \"));\n",
    "if num.is_integer():\n",
    "    print(\"Integer\")\n",
    "    if num % 2 ==0: \n",
    "        print(\"Even\");\n",
    "        num = 34;\n",
    "    else:\n",
    "        print(\"Odd\")\n",
    "else:\n",
    "    print(\"Float\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c35da82e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.  3.  6. 10. 15. 21. 28. 36. 45. 55.]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np;\n",
    "\n",
    "a_i = np.linspace(1, 10, 10) #  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
    "a_i_cumsum  = np.cumsum(a_i) # 累加求和\n",
    "print(a_i_cumsum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "99c9ff82",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1]\n",
      " [2]\n",
      " [3]]\n",
      "----------\n",
      "[[1 2 3]]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([[1, 2, 3]])\n",
    "b = a.T; #  转置\n",
    "print(b)\n",
    "print(\"----------\")\n",
    "print(b.T)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "187efe8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1  2  3  4  5  6  7  8  9 10]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np;\n",
    "\n",
    "# 启用交互模式（可选）\n",
    "plt.ion()\n",
    "a = np.arange(1, 11, 1)\n",
    "print(a)\n",
    "# 绘制散点图\n",
    "plt.scatter(a, a * 2)\n",
    "\n",
    "# 添加标题和坐标轴标签\n",
    "plt.title('Arithmetic Progression')\n",
    "plt.xlabel('Index, $n$')\n",
    "plt.ylabel('Value, $a_n$')\n",
    "\n",
    "# 显示图形\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b7d808b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hovertemplate": "species=setosa<br>sepal_width=%{x}<br>sepal_length=%{y}<extra></extra>",
         "legendgroup": "setosa",
         "marker": {
          "color": "#636efa",
          "symbol": "circle"
         },
         "mode": "markers",
         "name": "setosa",
         "orientation": "v",
         "showlegend": true,
         "type": "scatter",
         "x": {
          "bdata": "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",
          "dtype": "f8"
         },
         "xaxis": "x",
         "y": {
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     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.express as px\n",
    "\n",
    "# 创建交互式散点图\n",
    "df = px.data.iris()\n",
    "fig = px.scatter(df, x='sepal_width', y='sepal_length', color='species')\n",
    "fig.show()"
   ]
  }
 ],
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